End User representatives
Good estimates of landscape dryness underpin fire danger ratings, fire behaviour models, and flood prediction. Soil dryness also strongly influences heatwave development by driving the transfer of solar heating from the soil surface into air temperature rise.
Fire intensity, spread rate and ignition are sensitive to the fuel dryness, which is strongly linked to soil moisture content. Estimates and forecasts of fuel dryness and soil moisture are the foundation of the fire danger calculations used to rate and manage bushfires and to warn of developing fire danger.
Similarly, estimates and forecasts of soil moisture are essential ingredients to be able to forecast with accuracy river flows on a seasonal scales (one to three months), which is much in demand by water managers and reservoir operators.
This project is improving the ability to manage extreme events by developing a soil moisture analysis that makes use of many different sources of observations and cutting edge land-surface modelling and data assimilation. The new information is being calibrated with the old scheme so that it can be used within existing fire and flood forecasting prediction systems.
The project has focused on production of a historical dataset of the Keetch-Byram Drought Index (KBDI) and Mount’s Soil Dryness Index (MSDI) using analyses of rainfall and maximum temperature. This new gridded dataset of KBDI and MSDI will be compared with the much used, lower 25 kilometre resolution, Finkele-Mills dataset, and will be a valuable resource for researchers working on fire climatologies across Australia.
Research has also commenced on the development of a high resolution soil moisture system based around the Joint UK Land Environment Simulator model and the Joint UK Land Environment Simulator-based Australian Soil Moisture Information System. The work has also investigated calibration and rescaling of numerical weather prediction soil moisture measures, and inter-comparison of traditional soil dryness models (KBDI, MSDI) with soil moisture/dryness from numerical weather prediction models, satellite measures of landscape dryness and ground-based soil moisture observations.
End-users will benefit from more accurate, detailed and confident estimates and forecasts of soil moisture, and hence an expectation of more accurate predictions of fire danger and fire behaviour, flood forecasting, landslip warning and heatwaves. Datasets of landscape dryness will support a wide range of other research in fire, flood and heatwave prediction.
This project will improve Australia’s ability to manage extreme events by developing a state of the art, world’s best practice in soil moisture analysis.
Emerging new approaches to evaluate landscape dryness through the use of satellite remote sensing data, land surface modelling and data assimilation techniques are available, measuring dryness more systematically than the empirical methods. Satellite measurements can be blended with land surface model simulations to provide more accurate, detailed and confident estimates and forecasts of land dryness.
Soil dryness is a key component in operational fire danger rating systems.
|Resilience to clustered disaster events on the coast - storm surge||Dr Scott Nichol||Geoscience Australia|
|Improving flood forecast skill using remote sensing data||Assoc Prof Valentijn Pauwels||Monash University|
|Mapping bushfire hazard and impacts||Prof Albert van Dijk||Australian National University|
|Disaster landscape attribution: thermal anomaly surveillance and hazard mapping, data scaling and validation||Prof Simon Jones||RMIT University|